Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 25 December 2024 | Viewed by 15338
Special Issue Editors
Interests: hyperspectral remote sensing image processing; target detection; dimensionality reduction; classification; metric learning; transfer learning; deep learning; lithologic mapping; geological application of remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: distance metric learning; few-shot learning; hyperspectral image analysis; statistical classification
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral imagery; remote sensing; intelligent processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing image includes a rich description of the earth’s surface in various modalities (hyperspectral data, high resolution data, multispectral data, synthetic aperture radar (SAR) data, etc.). Remote sensing target detection or object detection aims to determine whether there are targets or objects of interest in the image, playing a decisive role in resource detection, environmental monitoring, urban planning, national security, agriculture, forestry, climate, hydrolog, etc. In recent years, artificial intelligence (AI) has achieved considerable development and been successfully applied for various applications, such as regression, clustering, classification, etc. Although AI-driven approaches can handle the massive quantities of data acquired by remote sensors, they require many high-quality labeled samples to deal with remote sensing big data, leading to fragile results. That is, AI-driven approaches with strong feature extraction abilities have limited performance and are still far from practical demands. Thus, target detection or object detection in the presence of complicated backgrounds with limited labeled samples remains a challenging mission. There is still much room for research on remote sensing target detection and object detection. The main goal of this Special Issue is to address advanced topics related to remote sensing target detection and object detection.
Topics of interests include but are not limited to the following:
- New AI-driven methods for remote sensing data, such as GNN, transformer, etc.;
- New remote sensing datasets, including hyperspectral, high resolution, SAR datasets, etc.;
- Machine learning techniques for remote sensing applications, such as domain adaptation, few-shot learning, manifold learning, and metric learning;
- Machine learning-based drone detection and fine-grained detection;
- Target detection, object detection, and anomaly detection;
- Data-driven applications in remote sensing;
- Technique reviews on related topics.
Dr. Yanni Dong
Dr. Xiaochen Yang
Prof. Dr. Qian Du
Guest Editors
Manuscript Submission Information
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Keywords
- remote sensing
- target detection
- artificial intelligence
- machine learning
- deep learning
- object detection
- new datasets
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